On Analyzing Process Compliance in Skin Cancer Treatment: An Experience Report from the Evidence-Based Medical Compliance Cluster (EBMC2)

Binder, Michael; Dorda, Wolfgang; Duftschmid, Georg; Dunkl, Reinhold; Fröschl, Karl Anton; Gall, Walter; Grossmann, Wilfried; Harmankaya, Kaan; Hronský, Milan; Rinderle-Ma, Stefanie; Rinner, Claus; Weber, Stefanie

doi:10.1007/978-3-642-31095-9_26

Cited by 25 publications

(17 citation statements)

References 9 publications

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“…This model consists of five stages: plan and justify, extract, create control-flow model and connect event log, create integrated process model, and operational support. Only one paper [57] clearly mentioned the L* lifecycle model as the methodology being used in the study. This implies limited awareness of a methodology proposed specifically for implementing process mining.…”

Section: E Methodologiesmentioning

confidence: 99%

“…Data limitations were identified in ten papers [20], [22], [33], [36], [37], [44], [48], [57], [60] and were mostly related to limited access to the data, data quality problems, attributes not available in the data being extracted, or the dataset available in inappropriate level of details. Technique limitations were identified in 13 papers [3], [21], [28], [31], [35], [38]- [40], [48], [50], [52], [63], [64] specifically if the study undertaken was done by implementing plug-ins/ functionalities available in tools.…”

Section: F Limitations and Future Workmentioning

confidence: 99%

“…Some papers identified data improvements [28]- [30], [36]- [38], [48], [57], [64] in terms of improving data quality, dimensionality, and complexity. A larger number of important technical improvements were identified in 20 papers [3], [21]- [23], [29]- [31], [35], [39]- [41], [44], [48]- [52], [58], [60], [64].…”

Section: F Limitations and Future Workmentioning

confidence: 99%

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Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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Section: E Methodologiesmentioning

confidence: 99%

Section: F Limitations and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

View full text Add to dashboard Cite

show abstract

“…A more specific study for oncology [ 104 ] counts 7/37 papers where PM4HC was adopted in measuring the compliance on CG. More recent papers concerning the application of CC with CGs are found in [ 105 ] for alcoholism, in [ 106 ] for the treatment of skin (melanoma) cancer, and in [ 107 , 108 ] for colon and rectal cancers.…”

Section: Pm For Healthcare: the Perspectives On Cgsmentioning

confidence: 99%

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

Gatta

Vallati

Fernández-Llatas

et al. 2020

IJERPH

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In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

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“…In declarative languages, an "open world" is assumed where everything is allowed unless it is explicitly forbidden. Declarative models are widely used in domains and applications where processes cannot be "straightjacketed" into a procedural model [7,15,16,25].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Declare Maps Based on Event Correlations

Bose

Maggi

Aalst

2013

Lecture Notes in Computer Science

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Abstract. Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.

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On Analyzing Process Compliance in Skin Cancer Treatment: An Experience Report from the Evidence-Based Medical Compliance Cluster (EBMC2)

Cited by 25 publications

References 9 publications

Process mining in oncology: A literature review

Process mining in oncology: A literature review

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

Enhancing Declare Maps Based on Event Correlations

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